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Machine Learning Engineer Masters Program makes you proficient in techniques like Supervised Learning, Unsupervised Learning and Natural Language Processing. It includes training on the latest advancements and technical approaches in Artificial Intelligence & Machine Learning such as Deep Learning, Graphical Models and Reinforcement Learning.
Machine Learning Curriculum
 Define Data Science
 Discuss the era of Data Science
 Describe the Role of a Data Scientist
 Illustrate the Life cycle of Data Science
 List the Tools used in Data Science
 State what role Big Data and Hadoop, Python, R and Machine Learning play in Data Science
 What is Data Science?
 What does Data Science involve?
 Era of Data Science
 Business Intelligence vs Data Science
 Life cycle of Data Science
 Tools of Data Science
 Introduction to Python
 Discuss Data Acquisition techniques
 List the different types of Data
 Evaluate Input Data
 Explain the Data Wrangling techniques
 Discuss Data Exploration
 Data Analysis Pipeline
 What is Data Extraction
 Types of Data
 Raw and Processed Data
 Data Wrangling
 Exploratory Data Analysis
 Visualization of Data
 Essential Python Revision
 Necessary Machine Learning Python libraries
 Define Machine Learning
 Discuss Machine Learning Use cases
 List the categories of Machine Learning
 Illustrate Supervised Learning Algorithms
 Identify and recognize machine learning algorithms around us
 Understand the various elements of machine learning algorithm like parameters, hyper parameters, loss function and optimization.
 Python Revision (numpy, Pandas, scikit learn, matplotlib)
 What is Machine Learning?
 Machine Learning UseCases
 Machine Learning Process Flow
 Machine Learning Categories
 Linear regression
 Gradient descent
 Understand What is Supervised Learning?
 Illustrate Logistic Regression
 Define Classification
 Explain different Types of Classifiers such as Decision Tree and Random Forest
 What is Classification and its use cases?
 What is Decision Tree?
 Algorithm for Decision Tree Induction
 Creating a Perfect Decision Tree
 Confusion Matrix
 What is Random Forest?
 Implementation of Logistic regression, Decision tree, Random forest
 Define the importance of Dimensions
 Explore PCA and its implementation
 Discuss LDA and its implementation
 Introduction to Dimensionality
 Why Dimensionality Reduction
 PCA
 Factor Analysis
 Scaling dimensional model
 LDA
 PCA
 Scaling
 Understand What is Naïve Bayes Classifier
 How Naïve Bayes Classifier works?
 Understand Support Vector Machine
 Illustrate How Support Vector Machine works?
 Hyperparameter optimization
 What is Naïve Bayes?
 How Naïve Bayes works?
 Implementing Naïve Bayes Classifier
 What is Support Vector Machine?
 Illustrate how Support Vector Machine works?
 Hyperparameter optimization
 Grid Search vs Random Search
 Implementation of Support Vector Machine for Classification
 Implementation of Naïve Bayes, SVM

 Define Unsupervised Learning
 Discuss the following Cluster Analysis
 What is Clustering & its Use Cases?
 What is Kmeans Clustering?
 How Kmeans algorithm works?
 How to do optimal clustering
 What is Cmeans Clustering?
 What is Hierarchical Clustering?
 How Hierarchical Clustering works?
 Implementing Kmeans Clustering
 Implementing Hierarchical Clustering
 Define Association Rules
 Learn the backend of recommendation engines and develop your own using python
 What are Association Rules?
 Association Rule Parameters
 Calculating Association Rule Parameters
 Recommendation Engines
 How Recommendation Engines work?
 Collaborative Filtering
 Content Based Filtering
 Apriori Algorithm
 Market Basket Analysis
 Explain the concept of Reinforcement Learning
 Generalize a problem using Reinforcement Learning
 Explain Markov’s Decision Process
 Demonstrate Q Learning
 What is Reinforcement Learning
 Why Reinforcement Learning
 Elements of Reinforcement Learning
 Exploration vs Exploitation dilemma
 Epsilon Greedy Algorithm
 Markov Decision Process (MDP)
 Q values and V values
 Q – Learning
 α values
 Calculating Reward
 Discounted Reward
 Calculating Optimal quantities
 Implementing Q Learning
 Setting up an Optimal Action
 Explain Time Series Analysis (TSA)
 Discuss the need of TSA
 Describe ARIMA modelling
 Forecast the time series model
 What is Time Series Analysis?
 Importance of TSA
 Components of TSA
 White Noise
 AR model
 MA model
 ARMA model
 ARIMA model
 Stationarity
 ACF & PACF
 Discuss Model Selection
 Define Boosting
 Express the need of Boosting
 Explain the working of Boosting algorithm
 What is Model Selection?
 Need of Model Selection
 Cross – Validation
 What is Boosting?
 How Boosting Algorithms work?
 Types of Boosting Algorithms
 Adaptive Boosting
 Cross Validation
 AdaBoost
 How to approach a project
 HandsOn project implementation
 What Industry expects
 Industry insights for the Machine Learning domain
 QA and Doubt Clearing Session
Machine Learning Description
Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience.
Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data preprocessing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and QLearning.
After completing this Machine Learning Certification Training using Python, you should be able to:
 Gain insight into the 'Roles' played by a Machine Learning Engineer
 Automate data analysis using python
 Describe Machine Learning
 Work with realtime data
 Learn tools and techniques for predictive modeling
 Discuss Machine Learning algorithms and their implementation
 Validate Machine Learning algorithms
 Explain Time Series and it’s related concepts
 Gain expertise to handle business in future, living the present
Python Machine Learning Certification Course is a good fit for the below professionals:
 Developers aspiring to be a ‘Machine Learning Engineer'
 Analytics Managers who are leading a team of analysts
 Business Analysts who want to understand Machine Learning (ML) Techniques
 Information Architects who want to gain expertise in Predictive Analytics
 'Python' professionals who want to design automatic predictive models
The prerequisites for the Machine Learning Certification Training using Python includes development experience with Python. Fundamentals of Data Analysis practised over any of the data analysis tools like SAS/R will be a plus. However, Python would be more advantageous. You will be provided with complimentary “Python Statistics for Data Science Course” as a selfpaced course once you enrol for the course.
Course Content
Curriculum is empty